Meet the team
Fiona Crawford
Dr Fiona Crawford joined UBDC in November 2022 as a Lecturer in Transport Planning. Prior to this she was working at the University of the West of England (UWE) in the Centre for Transport and Society and in the Data Science and Mathematics cluster.
She has a PhD in Transport Studies from the University of Leeds as well as master’s degrees in Social Research Methods (Social Policy) and Data Science and Analytics. She is a quantitative social scientist with a particular focus on travel behaviour and associated changes in working patterns and grocery shopping behaviour. She is passionate about supporting women in STEM (Science, Technology, Engineering and Mathematics).
Her research has included:
- Developing new methods to examine travel patterns using Bluetooth data from Greater Manchester (https://www.sciencedirect.com/science/article/pii/S096585641730352X – open access)
- Evaluation of sustainable transport interventions such as the Access WEST programme and the bus components of the Transforming Cities Fund
- Examining the impact of the Covid-19 pandemic on road traffic in Bristol using number plate data (https://transition-air.org.uk/wp-content/uploads/TRANSITION-DI-Project-Report-Dr-Fiona-Crawford.pdf)
- Exploring how data about working patterns is collected in national travel surveys
- Identification of suitable areas for a demand responsive transport scheme in Bristol
- Supporting trials investigating pedestrian and cyclist trust in autonomous vehicles
- Examining the complexity of school travel
Positions
Lecturer in Transport Planning
Publication links
Positions
Lecturer in Transport Planning
Contact Details
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Publications
Cyclist and pedestrian trust in automated vehicles: An on-road and simulator trial.
Perceived accessibility of employment sites by jobseekers and the potential relevance of employer-subsidised demand responsive transport to enhance the commute.
Analysing spatial intrapersonal variability of road users using point-to-point sensor data.
Segmenting travellers based on day-to-day variability in work-related travel behaviour.
Identifying road user classes based on repeated trip behaviour using Bluetooth data.
A statistical method for estimating predictable differences between daily traffic flow profiles.
Jointly funded by
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